Hackathon about smart cities. We have used data from sensors petra project to predict traffic congestion and study the speed between the sections. We also display all these results. Nosotros hemos usado los datos de sensores del proyecto petra para predecir atascos de tráfico y estudiar la velocidad entre los tramos. Tambien se realizaron visualizaciones de todos estos resultados. Team: MLDSGR https://github.com/MLDGR https://machinelearningdatascience.wordpress.com/ Contributors: @djgarciagil Diego García Gil https://es.linkedin.com/in/djgarciagil @cjferba Carlos Jesus Fernandez Basso https://es.linkedin.com/in/cjferba @baldan Francisco Javier Baldán Lozano https://es.linkedin.com/in/franciscojavierbaldanlozano/en CODE: http://mldgr.github.io/traffic-deep-intelligence/

1. SmartCity-Hackathon
Baldán Lozano, Francisco Javier
Fernández Basso, Carlos Jesús
García Gil, Diego Jesús

2. Problem

3. Data Preprocessing
● 160.192 steps
● Added detection time variable
● Merged data into one single dataset
● 14 attributes

4. Prediction Application
Purpose of this application
This application focuses on obtaining data in time series format in order to
be able to predict the average time each vehicle detection by each sensor. If
high detection times are obtained they can be associated with the
presence of traffic jams.

5. Data Format
1. Obtain the data time sampled / accumulated every second detection.
A time series is defined as a set of data recorded at equidistant time intervals.
So the natural first step of this application is to transform the original dataset
for the average detection time sampled every second (from this first
transformation to obtain new datasets sampled at different intervals is
facilitated, such as 1, 10 , 15, 30, 45 and 60 minutes)

6. Prediction objectives
2. Getting predictions one-step-ahead detection time.
In this case it was decided to make predictions of detection times in each
sensor in order to associate the high detection times recorded with the
presence of traffic jams.
In this case we have made predictions for 10, 15, 30, 45 and 60 minutes.
With the initial data and processing on those obtained in the last two months
have been obtained runtimes less than 2 minutes.

7. Prediction objectives (2)
This step generates an output file for prediction with 3 columns, which
contain the time axis in UNIX time, the actual data used in training the
model prediction and the set of output data contains the actual data to the
penultimate prediction point and the prediction of the last point (test),
respectively.

8. Additional comments
If you run the experiment on a Linux system, you can take advantage of
parallelization characteristics implemented in the code by increasing the
number of cores in these types of functions:
mclapply (x, function (x) {}, mc.cores = getOption ("mc.cores", 1))

9. Future works
The next step nature of this work is to associate a timeout threshold from
which a jam would be detected in the sensor. This threshold would be
defined by the data and the characteristics of the road where the sensor
is located.

10. Route Analysis
● Clean the data:
○ Date to UNIX time
○ Order dataset by time (time series)
○ Remove ids with frequency < 2
○ Add hour, minute, second, day and month variables
○ Check for vehicles that have passed for at least two different sensors, the same day, with
a difference of 3 hours max.
○ 3079 vehicles

11. Route Analysis (2)
● Checks for a given id if it has been detected previously by other sensor, if
that's the case, it calculates the average speed
● Results are most used routes and average speed
● Example for route 1010 - 1020:
idDisp Avg. Speed At
7D03FA3475165187CAC76AC5FAB84F6ABFEFA2CA 116.1 1444403586
BE8D87EF037736DED4F550E4E23A20A68B05A8C9 102.83 1444404979
183D25070470F6B27A27D3A50BC08BBF52BE3E24 81.38 1444407669